Model Optimization for Multi-Camera 3D Detection and Tracking

• URL: http://arxiv.org/abs/2602.00450v2
• Date: Tue, 03 Feb 2026 17:47:07 GMT
• Title: Model Optimization for Multi-Camera 3D Detection and Tracking
• Abstract summary: Outside-in multi-camera perception is increasingly important in indoor environments.<n>We evaluate Sparse4D, a query-based 3D detection and tracking framework.<n>We study reduced input frame rates, post-training quantization, transfer to the WILDTRACK benchmark, and Transformer Engine mixedprecision fine-tuning.
• Score: 13.756560739163362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Outside-in multi-camera perception is increasingly important in indoor environments, where networks of static cameras must support multi-target tracking under occlusion and heterogeneous viewpoints. We evaluate Sparse4D, a query-based spatiotemporal 3D detection and tracking framework that fuses multi-view features in a shared world frame and propagates sparse object queries via instance memory. We study reduced input frame rates, post-training quantization (INT8 and FP8), transfer to the WILDTRACK benchmark, and Transformer Engine mixed-precision fine-tuning. To better capture identity stability, we report Average Track Duration (AvgTrackDur), which measures identity persistence in seconds. Sparse4D remains stable under moderate FPS reductions, but below 2 FPS, identity association collapses even when detections are stable. Selective quantization of the backbone and neck offers the best speed-accuracy trade-off, while attention-related modules are consistently sensitive to low precision. On WILDTRACK, low-FPS pretraining yields large zero-shot gains over the base checkpoint, while small-scale fine-tuning provides limited additional benefit. Transformer Engine mixed precision reduces latency and improves camera scalability, but can destabilize identity propagation, motivating stability-aware validation.

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